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Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization

Khan, W and Salhi, A and Asif, M and Adeeb, R and Sulaiman, M (2015) 'Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization.' International Journal of Advanced Computer Science and Applications, 6 (12). 279 - 287. ISSN 2158-107X

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Abstract: Multi-objective EAs (MOEAs) are well established population-based techniques for solving various search and optimization problems. MOEAs employ different evolutionary operators to evolve populations of solutions for approximating the set of optimal solutions of the problem at hand in a single simulation run. Different evolutionary operators suite different problems. The use of multiple operators with a self-adaptive capability can further improve the performance of existing MOEAs. This paper suggests an enhanced version of a genetically adaptive multi-algorithm for multi-objective (AMAL-GAM) optimisation which includes differential evolution (DE), particle swarm optimization (PSO), simulated binary crossover (SBX), Pareto archive evolution strategy (PAES) and simplex crossover (SPX) for population evolution during the course of optimization. We examine the performance of this enhanced version of AMALGAM experimentally over two different test suites, the ZDT test problems and the test instances designed recently for the special session on MOEA?s competition at the Congress of Evolutionary Computing of 2009 (CEC?09). The suggested algorithm has found better approximate solutions on most test problems in terms of inverted generational distance (IGD) as the metric indicator. - See more at:

Item Type: Article
Uncontrolled Keywords: Multi-objective optimization, Multi-objective Evolu-tionary algorithms (MOEAs), Pareto Optimality, Multi-objective Memetic Algorithm (MOMAs)
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 06 Dec 2016 10:48
Last Modified: 17 Aug 2017 17:21

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